Data Streaming and Real Time Data Processing Training Course

This instructor-led, live training in South Africa (online or onsite) is aimed at advanced-level data professionals who wish to optimize data processing workflows, ensure data integrity, and implement robust data lakehouse solutions that can handle the complexities of modern big data applications.

By the end of this training, participants will be able to:

• Gain an in-depth understanding of Iceberg’s architecture, including metadata management and file layout.
• Configure Iceberg for optimal performance in various environments and integrate it with multiple data processing engines.
• Manage large-scale Iceberg tables, perform complex schema changes, and handle partition evolution.
• Master techniques to optimize query performance and data scan efficiency for large datasets.
• Implement mechanisms to ensure data consistency, manage transactional guarantees, and handle failures in distributed environments.

This instructor-led, live training in South Africa (online or onsite) is tailored for beginner-level data professionals who wish to acquire the knowledge and skills necessary to effectively utilise Apache Iceberg for managing large-scale datasets, ensuring data integrity, and optimising data processing workflows.

By the end of this training, participants will be able to:

• Gain a thorough understanding of Apache Iceberg's architecture, features, and benefits.
• Learn about table formats, partitioning, schema evolution, and time travel capabilities.
• Install and configure Apache Iceberg in different environments.
• Create, manage, and manipulate Iceberg tables.
• Understand the process of migrating data from other table formats to Iceberg.

This instructor-led live training in South Africa (online or onsite) is aimed at intermediate-level data scientists and engineers who wish to use Google Colab and Apache Spark for big data processing and analytics.

By the end of this training, participants will be able to:

• Set up a big data environment using Google Colab and Spark.
• Process and analyze large datasets efficiently with Apache Spark.
• Visualize big data in a collaborative environment.
• Integrate Apache Spark with cloud-based tools.

Technological advancements and the exponential growth of information are reshaping business operations across numerous sectors, including government. The volume of government data generation and digital archiving is surging, driven by the proliferation of mobile devices and applications, smart sensors and IoT devices, cloud computing solutions, and citizen-facing portals. As digital information becomes more expansive and complex, the management, processing, storage, security, and disposition of this data grow increasingly challenging. New tools for capture, search, discovery, and analysis are enabling organisations to extract valuable insights from unstructured data. The government sector is at a critical juncture, recognising information as a strategic asset. Governments must protect, leverage, and analyse both structured and unstructured data to enhance service delivery and meet mission requirements. As government leaders strive to evolve into data-driven organisations, they are laying the groundwork to correlate dependencies across events, people, processes, and information.

High-value government solutions will emerge from the integration of the most disruptive technologies:

• Mobile devices and applications
• Cloud services
• Social business technologies and networking
• Big Data and analytics

Big Data represents an intelligent industry solution that enables government bodies to make better decisions by acting on patterns revealed through the analysis of large volumes of data—both related and unrelated, structured and unstructured.

However, achieving these feats requires far more than simply accumulating massive quantities of data. "Making sense of these volumes of Big Data requires cutting-edge tools and technologies that can analyse and extract useful knowledge from vast and diverse streams of information," wrote Tom Kalil and Fen Zhao of the White House Office of Science and Technology Policy in a post on the OSTP Blog.

The White House took a significant step toward helping agencies identify these technologies by establishing the National Big Data Research and Development Initiative in 2012. This initiative allocated over $200 million to maximise the potential of the Big Data explosion and the tools required to analyse it.

The challenges posed by Big Data are nearly as daunting as its promise is encouraging. Efficient data storage is one such challenge. With budgets always tight, agencies must minimise the per-megabyte cost of storage while ensuring data remains easily accessible so users can retrieve it when and how they need it. Backing up massive quantities of data further intensifies this challenge.

Effective data analysis presents another major hurdle. Many agencies utilise commercial tools that allow them to sift through mountains of data, identifying trends that enhance operational efficiency. (A recent MeriTalk study found that federal IT executives believe Big Data could help agencies save over $500 billion while also fulfilling mission objectives.)

Custom-developed Big Data tools are also enabling agencies to address their data analysis needs. For instance, the Oak Ridge National Laboratory’s Computational Data Analytics Group has made its Piranha data analytics system available to other agencies. This system has helped medical researchers identify links that can alert doctors to aortic aneurysms before they occur. It is also employed for more routine tasks, such as sifting through resumes to connect job candidates with hiring managers.

Participants who complete this instructor-led, live training in South Africa will gain a practical, real-world understanding of Big Data and its related technologies, methodologies and tools.

Participants will have the opportunity to put this knowledge into practice through hands-on exercises. Group interaction and instructor feedback make up an important component of the class.

The course starts with an introduction to elemental concepts of Big Data, then progresses into the programming languages and methodologies used to perform Data Analysis. Finally, we discuss the tools and infrastructure that enable Big Data storage, Distributed Processing, and Scalability.

Informatica with Big Data (BDM) is a programme designed to equip data professionals with the skills to develop, manage, and analyse large datasets by leveraging the most recent technologies and architectures in the Big Data sector. The curriculum covers the entire lifecycle, from data ingestion, integration, cleansing, and curation to data analytics, as well as the exposure and consumption of big data services.
Participants will explore solutions that process extensive datasets using Big Data technologies and architectures such as Apache Hive, Apache Hadoop, and Apache Spark. The course also offers hands-on experience with Informatica tools like Bloombox, Big Data Management, and iData Fabric, providing a practical understanding of big data concepts like Map Reduce and Hadoop. Upon completion, learners will be capable of creating end-to-end data solutions using Informatica and its associated Big Data offerings.

Apache NiFi is an open-source, flow-based data integration and event-processing platform. It enables automated, real-time data routing, transformation, and system mediation between disparate systems, with a web-based UI and fine-grained control.

This instructor-led, live training (onsite or remote) is aimed at intermediate-level administrators and engineers who wish to deploy, manage, secure, and optimize NiFi dataflows in production environments.

By the end of this training, participants will be able to:

• Install, configure, and maintain Apache NiFi clusters.
• Design and manage dataflows from varied sources and sinks.
• Implement flow automation, routing, and transformation logic.
• Optimize performance, monitor operations, and troubleshoot issues.

Format of the Course

• Interactive lecture with real-world architecture discussion.
• Hands-on labs: building, deploying, and managing flows.
• Scenario-based exercises in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

This course offers a hands-on introduction to developing scalable data processing and Machine Learning workflows using PySpark. Participants will gain insight into how Apache Spark functions within contemporary Big Data ecosystems and how to efficiently manage large datasets by applying distributed computing principles.

This instructor-led, live training in South Africa (online or onsite) is aimed at engineers who wish to set up and deploy an Apache Spark system for processing very large amounts of data.

By the end of this training, participants will be able to:

• Install and configure Apache Spark.
• Quickly process and analyse very large data sets.
• Understand the difference between Apache Spark and Hadoop MapReduce and when to use which.
• Integrate Apache Spark with other machine learning tools.

This live, instructor-led training in South Africa (online or at your premises) is designed for system administrators with beginner to intermediate skills who want to deploy, maintain, and optimise Spark clusters.

At the end of this training, participants will be able to:

• Install and configure Apache Spark across various environments.
• Manage cluster resources and monitor Spark applications.
• Optimise Spark cluster performance.
• Implement security measures and ensure high availability.
• Debug and troubleshoot common Spark issues.

The initial learning curve for Apache Spark is steep, requiring significant effort before yielding results. This course is designed to help learners navigate that challenging first phase. Upon completion, participants will grasp the fundamentals of Apache Spark, clearly distinguish between RDDs and DataFrames, and become proficient with both Python and Scala APIs. They will also gain a solid understanding of executors, tasks, and other core concepts. Aligned with industry best practices, the course places a strong emphasis on cloud deployment, specifically within Databricks and AWS environments. Students will also learn to differentiate between AWS EMR and AWS Glue, one of AWS’s latest Spark services.

AUDIENCE:

Data Engineers, DevOps Professionals, Data Scientists

In this instructor-led, live training in South Africa, participants will learn how to use Python and Spark together to analyse big data as they work on hands-on exercises.

By the end of this training, participants will be able to:

• Learn how to use Spark with Python to analyse Big Data.
• Work on exercises that mimic real-world cases.
• Use different tools and techniques for big data analysis using PySpark.

This instructor-led, live training in South Africa (online or onsite) is aimed at developers who wish to use and integrate Spark, Hadoop, and Python to process, analyze, and transform large and complex data sets.

By the end of this training, participants will be able to:

• Set up the necessary environment to start processing big data with Spark, Hadoop, and Python.
• Understand the features, core components, and architecture of Spark and Hadoop.
• Learn how to integrate Spark, Hadoop, and Python for big data processing.
• Explore the tools in the Spark ecosystem (Spark MLlib, Spark Streaming, Kafka, Sqoop, Kafka, and Flume).
• Build collaborative filtering recommendation systems similar to Netflix, YouTube, Amazon, Spotify, and Google.
• Use Apache Mahout to scale machine learning algorithms.

Stratio is a data-centric platform that unifies big data, AI, and governance into a single solution. Its Rocket and Intelligence modules facilitate rapid data exploration, transformation, and advanced analytics within enterprise environments.

This instructor-led, live training (available online or onsite) is designed for intermediate-level data professionals seeking to effectively utilise the Rocket and Intelligence modules in Stratio with PySpark. The focus is on looping structures, user-defined functions, and advanced data logic.

By the conclusion of this training, participants will be able to:

• Navigate and operate within the Stratio platform using the Rocket and Intelligence modules.
• Apply PySpark for data ingestion, transformation, and analysis.
• Use loops and conditional logic to manage data workflows and feature engineering tasks.
• Create and manage user-defined functions (UDFs) for reusable data operations in PySpark.

Format of the Course

• Interactive lecture and discussion.
• Numerous exercises and practice sessions.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.